import json
import copy
import time
import random
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import matplotlib.pyplot as plt
from matplotlib import pyplot as plt
from torchsummary import summary
from nmfd_gnn import NMFD_GNN
print (torch.cuda.is_available())
device = torch.device("cuda:0")
random_seed = 42
random.seed(random_seed)
torch.manual_seed(random_seed)
torch.cuda.manual_seed(random_seed)
r = random.random
True
#1.1: settings
M = 10 #number of time interval in a window
missing_ratio = 0.50
file_name = "m_" + str(M) + "_missing_" + str(int(missing_ratio*100))
print (file_name)
#1.2: hyperparameters
num_epochs, batch_size, learning_rate = 200, 16, 0.001
beta_flow, beta_occ, beta_phy = 1.0, 1.0, 0.1
batch_size_vt = 16 #batch size for evaluation and test
delta_ratio = 0.1 #the ratio of delta in the standard deviation of flow
hyper = {"n_e": num_epochs, "b_s": batch_size, "b_s_vt": batch_size_vt, "l_r": learning_rate,\
"beta_f": beta_flow, "beta_o": beta_occ, "beta_p": beta_phy, "delta_ratio": delta_ratio}
gnn_dim_1, gnn_dim_2, gnn_dim_3, lstm_dim = 2, 128, 128, 128
p_dim = 10 #column dimension of L1, L2
c_k = 5.5 #meter, the sum of loop width and uniform vehicle length. based on Gero and Daganzo 2008.
theta_ini = [-2.757, 4.996, -2.409, 1.638, 3.569]
hyper_model = {"g_dim_1": gnn_dim_1, "g_dim_2": gnn_dim_2, "g_dim_3": gnn_dim_3, "l_dim": lstm_dim,\
"p_dim": p_dim, "c_k": c_k, "theta_ini": theta_ini}
max_no_decrease = 30
#1.3: set paths
root_path = "/home/umni2/a/umnilab/users/xue120/umni4/2023_mfd_traffic/"
file_path = root_path + "2_prepare_data/" + file_name + "/"
train_path, vali_path, test_path =\
file_path + "train.json", file_path + "vali.json", file_path + "test.json"
sensor_id_path = file_path + "sensor_id_order.json"
sensor_adj_path = file_path + "sensor_adj.json"
mean_std_path = file_path + "mean_std.json"
m_10_missing_50
def visualize_train_loss(total_phy_flow_occ_loss):
plt.figure(figsize=(4,3), dpi=75)
t_p_f_o_l = np.array(total_phy_flow_occ_loss)
e_loss, p_loss, f_loss, o_loss = t_p_f_o_l[:,0], t_p_f_o_l[:,1], t_p_f_o_l[:,2], t_p_f_o_l[:,3]
x = range(len(e_loss))
plt.plot(x, p_loss, linewidth=1, label = "phy loss")
plt.plot(x, f_loss, linewidth=1, label = "flow loss")
plt.plot(x, o_loss, linewidth=1, label = "occ loss")
plt.legend()
plt.title('Loss decline on train')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.savefig(file_name + '/' + 'train_loss.png', bbox_inches = 'tight')
plt.show()
def visualize_flow_loss(vali_f_mae, test_f_mae):
plt.figure(figsize=(4,3), dpi=75)
x = range(len(vali_f_mae))
plt.plot(x, vali_f_mae, linewidth=1, label="Validate")
plt.plot(x, test_f_mae, linewidth=1, label="Test")
plt.legend()
plt.title('MAE of flow on validate/test')
plt.xlabel('Epoch')
plt.ylabel('MAE (veh/h)')
plt.savefig(file_name + '/' + 'flow_mae.png', bbox_inches = 'tight')
plt.show()
def visualize_occ_loss(vali_o_mae, test_o_mae):
plt.figure(figsize=(4,3), dpi=75)
x = range(len(vali_o_mae))
plt.plot(x, vali_o_mae, linewidth=1, label="Validate")
plt.plot(x, test_o_mae, linewidth=1, label="Test")
plt.legend()
plt.title('MAE of occupancy on validate/test')
plt.xlabel('Epoch')
plt.ylabel('MAE')
plt.savefig(file_name + '/' + 'occ_mae.png',bbox_inches = 'tight')
plt.show()
def MAELoss(yhat, y):
return float(torch.mean(torch.div(torch.abs(yhat-y), 1)))
def RMSELoss(yhat, y):
return float(torch.sqrt(torch.mean((yhat-y)**2)))
def vali_test(model, f, f_mask, o, o_mask, f_o_mean_std, b_s_vt):
flow_std, occ_std, n = f_o_mean_std[1], f_o_mean_std[3], len(f)
f_mae_list, f_rmse_list, o_mae_list, o_rmse_list, num_list = list(), list(), list(), list(), list()
for i in range(0, n, b_s_vt):
s, e = i, np.min([i+b_s_vt, n])
num_list.append(e-s)
bf, bo, bf_mask, bo_mask = f[s: e], o[s: e], f_mask[s: e], o_mask[s: e]
bf_hat, bo_hat, bq_hat, bq_theta = model.run(bf_mask, bo_mask)
bf_hat, bo_hat = bf_hat.cpu(), bo_hat.cpu()
bf_mae, bf_rmse = MAELoss(bf_hat, bf)*flow_std, RMSELoss(bf_hat, bf)*flow_std
bo_mae, bo_rmse = MAELoss(bo_hat, bo)*occ_std, RMSELoss(bo_hat, bo)*occ_std
f_mae_list.append(bf_mae)
f_rmse_list.append(bf_rmse)
o_mae_list.append(bo_mae)
o_rmse_list.append(bo_rmse)
f_mae, o_mae = np.dot(f_mae_list, num_list)/n, np.dot(o_mae_list, num_list)/n
f_rmse = np.sqrt(np.dot(np.multiply(f_rmse_list, f_rmse_list), num_list)/n)
o_rmse = np.sqrt(np.dot(np.multiply(o_rmse_list, o_rmse_list), num_list)/n)
return f_mae, f_rmse, o_mae, o_rmse
def evaluate(model, vt_f, vt_o, vt_f_m, vt_o_m, f_o_mean_std, b_s_vt): #vt: vali_test
vt_f_mae, vt_f_rmse, vt_o_mae, vt_o_rmse =\
vali_test(model, vt_f, vt_f_m, vt_o, vt_o_m, f_o_mean_std, b_s_vt)
return vt_f_mae, vt_f_rmse, vt_o_mae, vt_o_rmse
import torch
#4.1: one training epoch
def train_epoch(model, opt, criterion, train_f_x, train_f_y, train_o_x, train_o_y, hyper, flow_std_squ, delta):
#f: flow; o: occupancy
model.train()
losses, p_losses, f_losses, o_losses = list(), list(), list(), list()
beta_f, beta_o, beta_p, b_s = hyper["beta_f"], hyper["beta_o"], hyper["beta_p"], hyper["b_s"]
n = len(train_f_x)
print ("# batch: ", int(n/b_s))
for i in range(0, n-b_s, b_s):
time1 = time.time()
x_f_batch, y_f_batch = train_f_x[i: i+b_s], train_f_y[i: i+b_s]
x_o_batch, y_o_batch = train_o_x[i: i+b_s], train_o_y[i: i+b_s]
opt.zero_grad()
y_f_hat, y_o_hat, q_hat, q_theta = model.run(x_f_batch, x_o_batch)
#p_loss = criterion(q_hat, q_theta).cpu() #physical loss
#p_loss = p_loss/flow_std_squ
#hinge loss
q_gap = q_hat - q_theta
delta_gap = torch.ones(q_gap.shape, device=device)*delta
zero_gap = torch.zeros(q_gap.shape, device=device) #(n, m)
hl_loss = torch.max(q_gap-delta_gap, zero_gap) + torch.max(-delta_gap-q_gap, zero_gap)
hl_loss = hl_loss/flow_std_squ
p_loss = criterion(hl_loss, zero_gap).cpu() #(n, m)
f_loss = criterion(y_f_hat.cpu(), y_f_batch) #data loss of flow
o_loss = criterion(y_o_hat.cpu(), y_o_batch) #data loss of occupancy
loss = beta_f*f_loss + beta_o*o_loss + beta_p*p_loss
loss.backward()
opt.step()
losses.append(loss.data.numpy())
p_losses.append(p_loss.data.numpy())
f_losses.append(f_loss.data.numpy())
o_losses.append(o_loss.data.numpy())
if i % (64*b_s) == 0:
print ("i_batch: ", i/b_s)
print ("the loss for this batch: ", loss.data.numpy())
print ("flow loss", f_loss.data.numpy())
print ("occ loss", o_loss.data.numpy())
time2 = time.time()
print ("time for this batch", time2-time1)
print ("----------------------------------")
n_loss = float(len(losses)+0.000001)
aver_loss = sum(losses)/n_loss
aver_p_loss = sum(p_losses)/n_loss
aver_f_loss = sum(f_losses)/n_loss
aver_o_loss = sum(o_losses)/n_loss
return aver_loss, model, aver_p_loss, aver_f_loss, aver_o_loss
#4.2: all train epochs
def train_process(model, criterion, train, vali, test, hyper, f_o_mean_std):
total_phy_flow_occ_loss = list()
n_mse_flow_occ = 0 #mse(flow) + mse(occ) for validation sets.
f_std = f_o_mean_std[1]
vali_f, vali_o = vali["flow"], vali["occupancy"]
vali_f_m, vali_o_m = vali["flow_mask"].to(device), vali["occupancy_mask"].to(device)
test_f, test_o = test["flow"], test["occupancy"]
test_f_m, test_o_m = test["flow_mask"].to(device), test["occupancy_mask"].to(device)
l_r, n_e = hyper["l_r"], hyper["n_e"]
opt = optim.Adam(model.parameters(), l_r, betas = (0.9,0.999), weight_decay=0.0001)
opt_scheduler = torch.optim.lr_scheduler.MultiStepLR(opt, milestones=[150])
print ("# epochs ", n_e)
r_vali_f_mae, r_vali_o_mae, r_test_f_mae, r_test_o_mae = list(), list(), list(), list()
r_vali_f_rmse, r_vali_o_rmse, r_test_f_rmse, r_test_o_rmse = list(), list(), list(), list()
flow_std_squ = np.power(f_std, 2)
no_decrease = 0
for i in range(n_e):
print ("----------------an epoch starts-------------------")
#time1_s = time.time()
time_s = time.time()
print ("i_epoch: ", i)
n_train = len(train["flow"])
number_list = copy.copy(list(range(n_train)))
random.shuffle(number_list, random = r)
shuffle_idx = torch.tensor(number_list)
train_x_f, train_y_f = train["flow_mask"][shuffle_idx], train["flow"][shuffle_idx]
train_x_o, train_y_o = train["occupancy_mask"][shuffle_idx], train["occupancy"][shuffle_idx]
delta = hyper["delta_ratio"] * f_std
aver_loss, model, aver_p_loss, aver_f_loss, aver_o_loss =\
train_epoch(model, opt, criterion, train_x_f.to(device), train_y_f,\
train_x_o.to(device), train_y_o, hyper, flow_std_squ, delta)
opt_scheduler.step()
total_phy_flow_occ_loss.append([aver_loss, aver_p_loss, aver_f_loss, aver_o_loss])
print ("train loss for this epoch: ", round(aver_loss, 6))
#evaluate
b_s_vt = hyper["b_s_vt"]
vali_f_mae, vali_f_rmse, vali_o_mae, vali_o_rmse =\
evaluate(model, vali_f, vali_o, vali_f_m, vali_o_m, f_o_mean_std, b_s_vt)
test_f_mae, test_f_rmse, test_o_mae, test_o_rmse =\
evaluate(model, test_f, test_o, test_f_m, test_o_m, f_o_mean_std, b_s_vt)
r_vali_f_mae.append(vali_f_mae)
r_test_f_mae.append(test_f_mae)
r_vali_o_mae.append(vali_o_mae)
r_test_o_mae.append(test_o_mae)
r_vali_f_rmse.append(vali_f_rmse)
r_test_f_rmse.append(test_f_rmse)
r_vali_o_rmse.append(vali_o_rmse)
r_test_o_rmse.append(test_o_rmse)
visualize_train_loss(total_phy_flow_occ_loss)
visualize_flow_loss(r_vali_f_mae, r_test_f_mae)
visualize_occ_loss(r_vali_o_mae, r_test_o_mae)
time_e = time.time()
print ("time for this epoch", time_e - time_s)
performance = {"train": total_phy_flow_occ_loss,\
"vali": [r_vali_f_mae, r_vali_f_rmse, r_vali_o_mae, r_vali_o_rmse],\
"test": [r_test_f_mae, r_test_f_rmse, r_test_o_mae, r_test_o_rmse]}
subfile = open(file_name + '/' + 'performance'+'.json','w')
json.dump(performance, subfile)
subfile.close()
#early stop
flow_std, occ_std = f_o_mean_std[1], f_o_mean_std[3]
norm_f_rmse, norm_o_rmse = vali_f_rmse/flow_std, vali_o_rmse/occ_std
norm_sum_mse = norm_f_rmse*norm_f_rmse + norm_o_rmse*norm_o_rmse
if n_mse_flow_occ > 0:
min_until_now = min([min_until_now, norm_sum_mse])
else:
min_until_now = 1000000.0
if norm_sum_mse > min_until_now:
no_decrease = no_decrease+1
else:
no_decrease = 0
if no_decrease == max_no_decrease:
print ("Early stop at the " + str(i+1) + "-th epoch")
return total_phy_flow_occ_loss, model
n_mse_flow_occ = n_mse_flow_occ + 1
print ("No_decrease: ", no_decrease)
return total_phy_flow_occ_loss, model
def tensorize(train_vali_test):
result = dict()
result["flow"] = torch.tensor(train_vali_test["flow"])
result["flow_mask"] = torch.tensor(train_vali_test["flow_mask"])
result["occupancy"] = torch.tensor(train_vali_test["occupancy"])
result["occupancy_mask"] = torch.tensor(train_vali_test["occupancy_mask"])
return result
def normalize_flow_occ(tvt, f_o_mean_std): #tvt: train, vali, test
#flow
f_mean, f_std = f_o_mean_std[0], f_o_mean_std[1]
f_mask, f = tvt["flow_mask"], tvt["flow"]
tvt["flow_mask"] = ((np.array(f_mask)-f_mean)/f_std).tolist()
tvt["flow"] = ((np.array(f)-f_mean)/f_std).tolist()
#occ
o_mean, o_std = f_o_mean_std[2], f_o_mean_std[3]
o_mask, o = tvt["occupancy_mask"], tvt["occupancy"]
tvt["occupancy_mask"] = ((np.array(o_mask)-o_mean)/o_std).tolist()
tvt["occupancy"] = ((np.array(o)-o_mean)/o_std).tolist()
return tvt
def transform_distance(d_matrix):
sigma, n_row, n_col = np.std(d_matrix), len(d_matrix), len(d_matrix[0])
sigma_square = sigma*sigma
for i in range(n_row):
for j in range(n_col):
d_i_j = d_matrix[i][j]
d_matrix[i][j] = np.exp(0.0-10000.0*d_i_j*d_i_j/sigma_square)
return d_matrix
def load_data(train_path, vali_path, test_path, sensor_adj_path, mean_std_path, sensor_id_path):
mean_std = json.load(open(mean_std_path))
f_mean, f_std, o_mean, o_std =\
mean_std["f_mean"], mean_std["f_std"], mean_std["o_mean"], mean_std["o_std"]
f_o_mean_std = [f_mean, f_std, o_mean, o_std]
train = json.load(open(train_path))
vali = json.load(open(vali_path))
test = json.load(open(test_path))
adj = json.load(open(sensor_adj_path))["adj"]
n_sensor = len(train["flow"][0])
train = tensorize(normalize_flow_occ(train, f_o_mean_std))
vali = tensorize(normalize_flow_occ(vali, f_o_mean_std))
test = tensorize(normalize_flow_occ(test, f_o_mean_std))
adj = torch.tensor(transform_distance(adj), device=device).float()
df_sensor_id = json.load(open(sensor_id_path))
sensor_length = [0.0 for i in range(n_sensor)]
for sensor in df_sensor_id:
sensor_length[df_sensor_id[sensor][0]] = df_sensor_id[sensor][3]
return train, vali, test, adj, n_sensor, f_o_mean_std, sensor_length
#6.1 load the data
time1 = time.time()
train, vali, test, adj, n_sensor, f_o_mean_std, sensor_length =\
load_data(train_path, vali_path, test_path, sensor_adj_path, mean_std_path, sensor_id_path)
time2 = time.time()
print (time2-time1)
11.363991022109985
print (len(train["flow"]))
print (len(vali["flow"]))
print (len(test["flow"]))
print (f_o_mean_std)
2007 663 663 [240.33475289317576, 220.77174415099844, 0.13747677267259475, 0.19174061724441546]
model = NMFD_GNN(n_sensor, M, hyper_model, f_o_mean_std, sensor_length, adj).to(device)
cri = nn.MSELoss()
#6.2: train the model
total_phy_flow_occ_loss, trained_model = train_process(model, cri, train, vali, test, hyper, f_o_mean_std)
# epochs 200 ----------------an epoch starts------------------- i_epoch: 0 # batch: 125 i_batch: 0.0 the loss for this batch: 1.8414308 flow loss 0.92753893 occ loss 0.9138899 time for this batch 0.8563082218170166 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.45965022 flow loss 0.16798665 occ loss 0.29166114 time for this batch 0.21345853805541992 ---------------------------------- train loss for this epoch: 0.587177
time for this epoch 38.55298638343811 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 1 # batch: 125 i_batch: 0.0 the loss for this batch: 0.35231787 flow loss 0.13469231 occ loss 0.21762325 time for this batch 0.21337890625 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.36744604 flow loss 0.13052775 occ loss 0.23691517 time for this batch 0.26166820526123047 ---------------------------------- train loss for this epoch: 0.392313
time for this epoch 38.482863664627075 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 2 # batch: 125 i_batch: 0.0 the loss for this batch: 0.33114082 flow loss 0.12518759 occ loss 0.20595041 time for this batch 0.20287013053894043 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.35361248 flow loss 0.11542369 occ loss 0.2381856 time for this batch 0.25232625007629395 ---------------------------------- train loss for this epoch: 0.355434
time for this epoch 36.6760196685791 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 3 # batch: 125 i_batch: 0.0 the loss for this batch: 0.32903153 flow loss 0.10883176 occ loss 0.22019683 time for this batch 0.16118454933166504 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.4018452 flow loss 0.12559749 occ loss 0.27624372 time for this batch 0.25580763816833496 ---------------------------------- train loss for this epoch: 0.335364
time for this epoch 40.8969669342041 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 4 # batch: 125 i_batch: 0.0 the loss for this batch: 0.34775785 flow loss 0.113414854 occ loss 0.23433991 time for this batch 0.19744467735290527 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.30532902 flow loss 0.09690206 occ loss 0.2084244 time for this batch 0.19155573844909668 ---------------------------------- train loss for this epoch: 0.323942
time for this epoch 36.409560680389404 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 5 # batch: 125 i_batch: 0.0 the loss for this batch: 0.28858554 flow loss 0.09510437 occ loss 0.19347851 time for this batch 0.20176434516906738 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.44640762 flow loss 0.12631658 occ loss 0.3200865 time for this batch 0.26904845237731934 ---------------------------------- train loss for this epoch: 0.315001
time for this epoch 40.9627161026001 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 6 # batch: 125 i_batch: 0.0 the loss for this batch: 0.2293528 flow loss 0.07938108 occ loss 0.14996979 time for this batch 0.23567724227905273 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.31748736 flow loss 0.099219225 occ loss 0.21826546 time for this batch 0.27227163314819336 ---------------------------------- train loss for this epoch: 0.308621
time for this epoch 42.028258085250854 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 7 # batch: 125 i_batch: 0.0 the loss for this batch: 0.359917 flow loss 0.10279307 occ loss 0.25712052 time for this batch 0.23253202438354492 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.23682478 flow loss 0.080204874 occ loss 0.15661769 time for this batch 0.2864696979522705 ---------------------------------- train loss for this epoch: 0.30278
time for this epoch 40.45991134643555 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 8 # batch: 125 i_batch: 0.0 the loss for this batch: 0.26747543 flow loss 0.0869026 occ loss 0.18057041 time for this batch 0.21356415748596191 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.19863705 flow loss 0.06230309 occ loss 0.13633226 time for this batch 0.25872015953063965 ---------------------------------- train loss for this epoch: 0.299851
time for this epoch 40.90656137466431 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 9 # batch: 125 i_batch: 0.0 the loss for this batch: 0.38338393 flow loss 0.10330619 occ loss 0.28007442 time for this batch 0.24542784690856934 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.31288683 flow loss 0.08749831 occ loss 0.22538568 time for this batch 0.28807854652404785 ---------------------------------- train loss for this epoch: 0.295074
time for this epoch 42.01219916343689 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 10 # batch: 125 i_batch: 0.0 the loss for this batch: 0.30172086 flow loss 0.08286461 occ loss 0.21885397 time for this batch 0.17701101303100586 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1774476 flow loss 0.05980489 occ loss 0.117640994 time for this batch 0.2378087043762207 ---------------------------------- train loss for this epoch: 0.290899
time for this epoch 41.144152879714966 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 11 # batch: 125 i_batch: 0.0 the loss for this batch: 0.21729904 flow loss 0.072451875 occ loss 0.14484504 time for this batch 0.19814038276672363 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.3110581 flow loss 0.084993854 occ loss 0.22606127 time for this batch 0.2786521911621094 ---------------------------------- train loss for this epoch: 0.288089
time for this epoch 41.984994888305664 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 12 # batch: 125 i_batch: 0.0 the loss for this batch: 0.23095897 flow loss 0.07241725 occ loss 0.15853967 time for this batch 0.22017216682434082 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.29783103 flow loss 0.08573996 occ loss 0.21208805 time for this batch 0.2705237865447998 ---------------------------------- train loss for this epoch: 0.285921
time for this epoch 41.82296586036682 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 13 # batch: 125 i_batch: 0.0 the loss for this batch: 0.39048958 flow loss 0.10101222 occ loss 0.28947335 time for this batch 0.20934724807739258 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.2531637 flow loss 0.07855044 occ loss 0.17461075 time for this batch 0.25981950759887695 ---------------------------------- train loss for this epoch: 0.281732
time for this epoch 41.781925678253174 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 14 # batch: 125 i_batch: 0.0 the loss for this batch: 0.28371674 flow loss 0.0718159 occ loss 0.21189791 time for this batch 0.2093338966369629 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.33691236 flow loss 0.090220265 occ loss 0.24668878 time for this batch 0.27156543731689453 ---------------------------------- train loss for this epoch: 0.279631
time for this epoch 41.96280789375305 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 15 # batch: 125 i_batch: 0.0 the loss for this batch: 0.26893407 flow loss 0.08602584 occ loss 0.18290508 time for this batch 0.22816872596740723 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.32263392 flow loss 0.08842536 occ loss 0.234205 time for this batch 0.25699853897094727 ---------------------------------- train loss for this epoch: 0.27791
time for this epoch 42.53934407234192 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 16 # batch: 125 i_batch: 0.0 the loss for this batch: 0.2622934 flow loss 0.0724553 occ loss 0.18983549 time for this batch 0.19074463844299316 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.2571864 flow loss 0.07567566 occ loss 0.18150793 time for this batch 0.26926493644714355 ---------------------------------- train loss for this epoch: 0.273905
time for this epoch 41.66872477531433 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 17 # batch: 125 i_batch: 0.0 the loss for this batch: 0.31030566 flow loss 0.08076872 occ loss 0.2295345 time for this batch 0.20749306678771973 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.22077945 flow loss 0.07936062 occ loss 0.14141622 time for this batch 0.2544131278991699 ---------------------------------- train loss for this epoch: 0.27221
time for this epoch 40.98801779747009 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 18 # batch: 125 i_batch: 0.0 the loss for this batch: 0.23837574 flow loss 0.067498796 occ loss 0.17087454 time for this batch 0.2258148193359375 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.21676534 flow loss 0.06688463 occ loss 0.1498782 time for this batch 0.26673054695129395 ---------------------------------- train loss for this epoch: 0.271173
time for this epoch 40.42730116844177 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 19 # batch: 125 i_batch: 0.0 the loss for this batch: 0.31564602 flow loss 0.0915236 occ loss 0.22411901 time for this batch 0.23389101028442383 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.19984283 flow loss 0.059440665 occ loss 0.14040017 time for this batch 0.26729345321655273 ---------------------------------- train loss for this epoch: 0.270346
time for this epoch 41.8628625869751 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 20 # batch: 125 i_batch: 0.0 the loss for this batch: 0.19777612 flow loss 0.059666242 occ loss 0.13810794 time for this batch 0.2031114101409912 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.19217199 flow loss 0.05998134 occ loss 0.13218854 time for this batch 0.2673203945159912 ---------------------------------- train loss for this epoch: 0.266878
time for this epoch 40.95800161361694 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 21 # batch: 125 i_batch: 0.0 the loss for this batch: 0.3207984 flow loss 0.09282313 occ loss 0.22797197 time for this batch 0.23520421981811523 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.25230244 flow loss 0.073977344 occ loss 0.17832212 time for this batch 0.28711915016174316 ---------------------------------- train loss for this epoch: 0.264954
time for this epoch 41.68501543998718 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 22 # batch: 125 i_batch: 0.0 the loss for this batch: 0.3002748 flow loss 0.078140445 occ loss 0.22213136 time for this batch 0.14823031425476074 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.21743105 flow loss 0.06585655 occ loss 0.15157181 time for this batch 0.26244163513183594 ---------------------------------- train loss for this epoch: 0.264003
time for this epoch 41.020365715026855 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 23 # batch: 125 i_batch: 0.0 the loss for this batch: 0.27240628 flow loss 0.07974536 occ loss 0.19265807 time for this batch 0.20927691459655762 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.20353127 flow loss 0.06433734 occ loss 0.13919166 time for this batch 0.2827889919281006 ---------------------------------- train loss for this epoch: 0.262331
time for this epoch 42.18397283554077 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 24 # batch: 125 i_batch: 0.0 the loss for this batch: 0.23034048 flow loss 0.0649897 occ loss 0.16534798 time for this batch 0.23336386680603027 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.25201347 flow loss 0.07650952 occ loss 0.17550077 time for this batch 0.2786085605621338 ---------------------------------- train loss for this epoch: 0.259506
time for this epoch 43.78471255302429 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 25 # batch: 125 i_batch: 0.0 the loss for this batch: 0.2939579 flow loss 0.076458715 occ loss 0.21749598 time for this batch 0.2827460765838623 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.27025634 flow loss 0.07978802 occ loss 0.19046497 time for this batch 0.27390336990356445 ---------------------------------- train loss for this epoch: 0.258613
time for this epoch 43.781771659851074 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 26 # batch: 125 i_batch: 0.0 the loss for this batch: 0.26372555 flow loss 0.075827956 occ loss 0.18789448 time for this batch 0.21874785423278809 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.19777387 flow loss 0.061935812 occ loss 0.13583581 time for this batch 0.27890777587890625 ---------------------------------- train loss for this epoch: 0.256407
time for this epoch 41.92899250984192 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 27 # batch: 125 i_batch: 0.0 the loss for this batch: 0.19545592 flow loss 0.05884064 occ loss 0.13661303 time for this batch 0.22065210342407227 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.21640669 flow loss 0.069536775 occ loss 0.14686774 time for this batch 0.24285030364990234 ---------------------------------- train loss for this epoch: 0.255269
time for this epoch 41.37053155899048 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 28 # batch: 125 i_batch: 0.0 the loss for this batch: 0.23868074 flow loss 0.072132014 occ loss 0.166546 time for this batch 0.23404264450073242 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.29006562 flow loss 0.07844192 occ loss 0.21162091 time for this batch 0.27571606636047363 ---------------------------------- train loss for this epoch: 0.253693
time for this epoch 42.034462451934814 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 29 # batch: 125 i_batch: 0.0 the loss for this batch: 0.30590454 flow loss 0.0776983 occ loss 0.22820322 time for this batch 0.2087714672088623 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.28522795 flow loss 0.079841286 occ loss 0.2053834 time for this batch 0.2841835021972656 ---------------------------------- train loss for this epoch: 0.253401
time for this epoch 41.53965520858765 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 30 # batch: 125 i_batch: 0.0 the loss for this batch: 0.17239751 flow loss 0.050254025 occ loss 0.12214154 time for this batch 0.2331235408782959 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.27989775 flow loss 0.07544826 occ loss 0.20444661 time for this batch 0.2568094730377197 ---------------------------------- train loss for this epoch: 0.251431
time for this epoch 41.88278651237488 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 31 # batch: 125 i_batch: 0.0 the loss for this batch: 0.1845639 flow loss 0.058050126 occ loss 0.12651177 time for this batch 0.2239704132080078 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.2504021 flow loss 0.07284675 occ loss 0.17755237 time for this batch 0.2834770679473877 ---------------------------------- train loss for this epoch: 0.250066
time for this epoch 41.758960485458374 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 32 # batch: 125 i_batch: 0.0 the loss for this batch: 0.14841044 flow loss 0.05169462 occ loss 0.09671416 time for this batch 0.2072460651397705 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.25223988 flow loss 0.06670359 occ loss 0.18553351 time for this batch 0.24680519104003906 ---------------------------------- train loss for this epoch: 0.251804
time for this epoch 41.487696409225464 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 33 # batch: 125 i_batch: 0.0 the loss for this batch: 0.15456451 flow loss 0.04820431 occ loss 0.10635851 time for this batch 0.22594022750854492 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.28128454 flow loss 0.08163659 occ loss 0.19964458 time for this batch 0.21125078201293945 ---------------------------------- train loss for this epoch: 0.24891
time for this epoch 40.78103303909302 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 34 # batch: 125 i_batch: 0.0 the loss for this batch: 0.22518888 flow loss 0.0696722 occ loss 0.15551397 time for this batch 0.24277210235595703 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18728565 flow loss 0.05942203 occ loss 0.12786134 time for this batch 0.28765082359313965 ---------------------------------- train loss for this epoch: 0.24662
time for this epoch 42.79044532775879 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 35 # batch: 125 i_batch: 0.0 the loss for this batch: 0.2803213 flow loss 0.078496926 occ loss 0.20182058 time for this batch 0.23141789436340332 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1861556 flow loss 0.057315033 occ loss 0.12883846 time for this batch 0.2705566883087158 ---------------------------------- train loss for this epoch: 0.246866
time for this epoch 40.86542367935181 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 36 # batch: 125 i_batch: 0.0 the loss for this batch: 0.26851544 flow loss 0.074023634 occ loss 0.19448878 time for this batch 0.21842455863952637 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.28146464 flow loss 0.082633354 occ loss 0.19882771 time for this batch 0.2658841609954834 ---------------------------------- train loss for this epoch: 0.245788
time for this epoch 41.10043382644653 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 37 # batch: 125 i_batch: 0.0 the loss for this batch: 0.3166088 flow loss 0.08244415 occ loss 0.23416162 time for this batch 0.17280006408691406 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.25992987 flow loss 0.07314552 occ loss 0.18678135 time for this batch 0.2594292163848877 ---------------------------------- train loss for this epoch: 0.245806
time for this epoch 41.91363716125488 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 38 # batch: 125 i_batch: 0.0 the loss for this batch: 0.21781844 flow loss 0.0696523 occ loss 0.14816374 time for this batch 0.2400829792022705 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.27088434 flow loss 0.073750064 occ loss 0.19713148 time for this batch 0.28087902069091797 ---------------------------------- train loss for this epoch: 0.243536
time for this epoch 41.94390082359314 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 39 # batch: 125 i_batch: 0.0 the loss for this batch: 0.26977247 flow loss 0.06776723 occ loss 0.20200236 time for this batch 0.2314596176147461 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.25797474 flow loss 0.07904191 occ loss 0.1789293 time for this batch 0.268707275390625 ---------------------------------- train loss for this epoch: 0.243398
time for this epoch 41.088252544403076 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 40 # batch: 125 i_batch: 0.0 the loss for this batch: 0.23710899 flow loss 0.07066212 occ loss 0.16644365 time for this batch 0.2319347858428955 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.25292194 flow loss 0.06724407 occ loss 0.18567508 time for this batch 0.26622867584228516 ---------------------------------- train loss for this epoch: 0.242674
time for this epoch 41.14931130409241 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 41 # batch: 125 i_batch: 0.0 the loss for this batch: 0.21543492 flow loss 0.06684609 occ loss 0.14858593 time for this batch 0.22763657569885254 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.22089633 flow loss 0.06647388 occ loss 0.15441999 time for this batch 0.28270387649536133 ---------------------------------- train loss for this epoch: 0.242132
time for this epoch 41.96283316612244 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 42 # batch: 125 i_batch: 0.0 the loss for this batch: 0.22940822 flow loss 0.06785363 occ loss 0.16155206 time for this batch 0.24377155303955078 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.267352 flow loss 0.0801972 occ loss 0.18715137 time for this batch 0.2729218006134033 ---------------------------------- train loss for this epoch: 0.241839
time for this epoch 41.81826853752136 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 43 # batch: 125 i_batch: 0.0 the loss for this batch: 0.24201745 flow loss 0.06653492 occ loss 0.17547947 time for this batch 0.26564455032348633 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15275988 flow loss 0.049519192 occ loss 0.10323894 time for this batch 0.267697811126709 ---------------------------------- train loss for this epoch: 0.240476
time for this epoch 41.50154495239258 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 44 # batch: 125 i_batch: 0.0 the loss for this batch: 0.2307256 flow loss 0.06269788 occ loss 0.16802499 time for this batch 0.20597267150878906 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.22507973 flow loss 0.061811756 occ loss 0.16326539 time for this batch 0.2798471450805664 ---------------------------------- train loss for this epoch: 0.240497
time for this epoch 40.25916075706482 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 45 # batch: 125 i_batch: 0.0 the loss for this batch: 0.21366076 flow loss 0.06418137 occ loss 0.14947638 time for this batch 0.23711609840393066 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.2751279 flow loss 0.073575236 occ loss 0.20154937 time for this batch 0.275968074798584 ---------------------------------- train loss for this epoch: 0.240259
time for this epoch 41.044926166534424 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 46 # batch: 125 i_batch: 0.0 the loss for this batch: 0.19972643 flow loss 0.06434933 occ loss 0.13537459 time for this batch 0.24289393424987793 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.28022254 flow loss 0.074966766 occ loss 0.20525222 time for this batch 0.27210521697998047 ---------------------------------- train loss for this epoch: 0.238622
time for this epoch 41.13613772392273 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 47 # batch: 125 i_batch: 0.0 the loss for this batch: 0.26079506 flow loss 0.08136107 occ loss 0.17943123 time for this batch 0.21535491943359375 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.23882875 flow loss 0.06660217 occ loss 0.17222361 time for this batch 0.2757711410522461 ---------------------------------- train loss for this epoch: 0.24061
time for this epoch 41.32854652404785 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 48 # batch: 125 i_batch: 0.0 the loss for this batch: 0.2708486 flow loss 0.07165233 occ loss 0.19919315 time for this batch 0.22971105575561523 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.22470511 flow loss 0.06773043 occ loss 0.15697223 time for this batch 0.2869706153869629 ---------------------------------- train loss for this epoch: 0.238984
time for this epoch 42.12303566932678 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 49 # batch: 125 i_batch: 0.0 the loss for this batch: 0.26478952 flow loss 0.06791037 occ loss 0.19687603 time for this batch 0.23865866661071777 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.30252996 flow loss 0.0723655 occ loss 0.23016159 time for this batch 0.25132250785827637 ---------------------------------- train loss for this epoch: 0.236611
time for this epoch 41.77879548072815 No_decrease: 4 ----------------an epoch starts------------------- i_epoch: 50 # batch: 125 i_batch: 0.0 the loss for this batch: 0.22394107 flow loss 0.07003973 occ loss 0.15389822 time for this batch 0.19198870658874512 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16482924 flow loss 0.04989575 occ loss 0.11493146 time for this batch 0.27187204360961914 ---------------------------------- train loss for this epoch: 0.236511
time for this epoch 42.3287250995636 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 51 # batch: 125 i_batch: 0.0 the loss for this batch: 0.27259412 flow loss 0.072005644 occ loss 0.20058496 time for this batch 0.2225797176361084 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17057993 flow loss 0.049272183 occ loss 0.1213058 time for this batch 0.265413761138916 ---------------------------------- train loss for this epoch: 0.237859
time for this epoch 42.6271436214447 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 52 # batch: 125 i_batch: 0.0 the loss for this batch: 0.13954811 flow loss 0.04150943 occ loss 0.09803713 time for this batch 0.23494291305541992 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.29853383 flow loss 0.077072755 occ loss 0.22145785 time for this batch 0.27828073501586914 ---------------------------------- train loss for this epoch: 0.236755
time for this epoch 41.6546585559845 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 53 # batch: 125 i_batch: 0.0 the loss for this batch: 0.35413265 flow loss 0.08601096 occ loss 0.26811767 time for this batch 0.24108290672302246 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.27366218 flow loss 0.0708987 occ loss 0.20276068 time for this batch 0.2732582092285156 ---------------------------------- train loss for this epoch: 0.235839
time for this epoch 41.75529193878174 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 54 # batch: 125 i_batch: 0.0 the loss for this batch: 0.25286707 flow loss 0.067940444 occ loss 0.18492398 time for this batch 0.21805167198181152 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.29418078 flow loss 0.07862101 occ loss 0.21555601 time for this batch 0.27674198150634766 ---------------------------------- train loss for this epoch: 0.235396
time for this epoch 41.89839220046997 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 55 # batch: 125 i_batch: 0.0 the loss for this batch: 0.24594778 flow loss 0.07038941 occ loss 0.17555544 time for this batch 0.2189779281616211 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.2277434 flow loss 0.06383535 occ loss 0.16390567 time for this batch 0.27669787406921387 ---------------------------------- train loss for this epoch: 0.235576
time for this epoch 41.25068259239197 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 56 # batch: 125 i_batch: 0.0 the loss for this batch: 0.21428227 flow loss 0.05929067 occ loss 0.15498929 time for this batch 0.21587085723876953 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.22546466 flow loss 0.065730244 occ loss 0.15973218 time for this batch 0.26003551483154297 ---------------------------------- train loss for this epoch: 0.23479
time for this epoch 42.20950508117676 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 57 # batch: 125 i_batch: 0.0 the loss for this batch: 0.23544924 flow loss 0.06676033 occ loss 0.16868562 time for this batch 0.2706787586212158 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.26058158 flow loss 0.070931725 occ loss 0.18964678 time for this batch 0.2614607810974121 ---------------------------------- train loss for this epoch: 0.234689
time for this epoch 41.941227436065674 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 58 # batch: 125 i_batch: 0.0 the loss for this batch: 0.29303578 flow loss 0.07839674 occ loss 0.21463518 time for this batch 0.2645082473754883 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.21292523 flow loss 0.058307108 occ loss 0.15461567 time for this batch 0.2754666805267334 ---------------------------------- train loss for this epoch: 0.234621
time for this epoch 43.64133954048157 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 59 # batch: 125 i_batch: 0.0 the loss for this batch: 0.1903666 flow loss 0.05954063 occ loss 0.13082351 time for this batch 0.24034762382507324 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.22620767 flow loss 0.06204022 occ loss 0.16416427 time for this batch 0.28040218353271484 ---------------------------------- train loss for this epoch: 0.233526
time for this epoch 41.244598388671875 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 60 # batch: 125 i_batch: 0.0 the loss for this batch: 0.21300173 flow loss 0.06433138 occ loss 0.14866771 time for this batch 0.22133302688598633 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.23951547 flow loss 0.06289869 occ loss 0.17661375 time for this batch 0.27775096893310547 ---------------------------------- train loss for this epoch: 0.234641
time for this epoch 44.08344483375549 No_decrease: 4 ----------------an epoch starts------------------- i_epoch: 61 # batch: 125 i_batch: 0.0 the loss for this batch: 0.24006954 flow loss 0.06628541 occ loss 0.17378137 time for this batch 0.2300126552581787 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18168435 flow loss 0.05128265 occ loss 0.13039954 time for this batch 0.26367616653442383 ---------------------------------- train loss for this epoch: 0.233262
time for this epoch 42.84070420265198 No_decrease: 5 ----------------an epoch starts------------------- i_epoch: 62 # batch: 125 i_batch: 0.0 the loss for this batch: 0.19604118 flow loss 0.05711527 occ loss 0.13892348 time for this batch 0.2388167381286621 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.2803127 flow loss 0.07747343 occ loss 0.20283599 time for this batch 0.27002859115600586 ---------------------------------- train loss for this epoch: 0.232411
time for this epoch 41.81299138069153 No_decrease: 6 ----------------an epoch starts------------------- i_epoch: 63 # batch: 125 i_batch: 0.0 the loss for this batch: 0.22629365 flow loss 0.059519976 occ loss 0.1667716 time for this batch 0.23772883415222168 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.23676449 flow loss 0.069682054 occ loss 0.16707934 time for this batch 0.2672078609466553 ---------------------------------- train loss for this epoch: 0.232456
time for this epoch 42.337172508239746 No_decrease: 7 ----------------an epoch starts------------------- i_epoch: 64 # batch: 125 i_batch: 0.0 the loss for this batch: 0.25797018 flow loss 0.067976326 occ loss 0.1899906 time for this batch 0.2072889804840088 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.3022592 flow loss 0.07300302 occ loss 0.22925326 time for this batch 0.28032994270324707 ---------------------------------- train loss for this epoch: 0.232081
time for this epoch 41.24631381034851 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 65 # batch: 125 i_batch: 0.0 the loss for this batch: 0.23990403 flow loss 0.066396624 occ loss 0.17350468 time for this batch 0.21597576141357422 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.2985823 flow loss 0.076931745 occ loss 0.22164688 time for this batch 0.2557492256164551 ---------------------------------- train loss for this epoch: 0.231326
time for this epoch 41.13559031486511 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 66 # batch: 125 i_batch: 0.0 the loss for this batch: 0.17269409 flow loss 0.048701037 occ loss 0.12399069 time for this batch 0.23293089866638184 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.22518955 flow loss 0.072024345 occ loss 0.15316248 time for this batch 0.269197940826416 ---------------------------------- train loss for this epoch: 0.230385
time for this epoch 41.70528483390808 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 67 # batch: 125 i_batch: 0.0 the loss for this batch: 0.26929533 flow loss 0.07363257 occ loss 0.1956591 time for this batch 0.2330162525177002 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.21482153 flow loss 0.0606987 occ loss 0.15411977 time for this batch 0.2798597812652588 ---------------------------------- train loss for this epoch: 0.231396
time for this epoch 41.43988490104675 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 68 # batch: 125 i_batch: 0.0 the loss for this batch: 0.1823574 flow loss 0.053852692 occ loss 0.12850223 time for this batch 0.24834775924682617 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.2554196 flow loss 0.06715931 occ loss 0.18825717 time for this batch 0.24804353713989258 ---------------------------------- train loss for this epoch: 0.23111
time for this epoch 41.638182163238525 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 69 # batch: 125 i_batch: 0.0 the loss for this batch: 0.18190786 flow loss 0.05957433 occ loss 0.122331485 time for this batch 0.20174479484558105 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16822173 flow loss 0.04620324 occ loss 0.122016504 time for this batch 0.24509310722351074 ---------------------------------- train loss for this epoch: 0.230039
time for this epoch 41.17905640602112 No_decrease: 4 ----------------an epoch starts------------------- i_epoch: 70 # batch: 125 i_batch: 0.0 the loss for this batch: 0.25584415 flow loss 0.07373919 occ loss 0.182102 time for this batch 0.21828007698059082 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.22296053 flow loss 0.063603505 occ loss 0.15935414 time for this batch 0.2734956741333008 ---------------------------------- train loss for this epoch: 0.229927
time for this epoch 42.03036093711853 No_decrease: 5 ----------------an epoch starts------------------- i_epoch: 71 # batch: 125 i_batch: 0.0 the loss for this batch: 0.28136772 flow loss 0.07189265 occ loss 0.20947167 time for this batch 0.2047586441040039 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.2532812 flow loss 0.069291525 occ loss 0.18398707 time for this batch 0.24064397811889648 ---------------------------------- train loss for this epoch: 0.230344
time for this epoch 42.032185554504395 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 72 # batch: 125 i_batch: 0.0 the loss for this batch: 0.2469576 flow loss 0.066514015 occ loss 0.18044071 time for this batch 0.2347571849822998 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.23504862 flow loss 0.06359307 occ loss 0.17145264 time for this batch 0.2872464656829834 ---------------------------------- train loss for this epoch: 0.230055
time for this epoch 41.73973894119263 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 73 # batch: 125 i_batch: 0.0 the loss for this batch: 0.19880849 flow loss 0.058283947 occ loss 0.14052165 time for this batch 0.2162022590637207 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.25293875 flow loss 0.07175293 occ loss 0.18118227 time for this batch 0.2678062915802002 ---------------------------------- train loss for this epoch: 0.228217
time for this epoch 41.64320683479309 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 74 # batch: 125 i_batch: 0.0 the loss for this batch: 0.32239988 flow loss 0.080274716 occ loss 0.24212143 time for this batch 0.21129226684570312 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.22052734 flow loss 0.06282087 occ loss 0.15770333 time for this batch 0.279437780380249 ---------------------------------- train loss for this epoch: 0.229702
time for this epoch 41.94496417045593 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 75 # batch: 125 i_batch: 0.0 the loss for this batch: 0.19973174 flow loss 0.056763556 occ loss 0.14296544 time for this batch 0.20404505729675293 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.26889843 flow loss 0.07246931 occ loss 0.19642626 time for this batch 0.2765214443206787 ---------------------------------- train loss for this epoch: 0.228939
time for this epoch 41.78610634803772 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 76 # batch: 125 i_batch: 0.0 the loss for this batch: 0.23010777 flow loss 0.057520706 occ loss 0.1725845 time for this batch 0.23522090911865234 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16722693 flow loss 0.04741858 occ loss 0.119806185 time for this batch 0.23984527587890625 ---------------------------------- train loss for this epoch: 0.228746
time for this epoch 42.28582262992859 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 77 # batch: 125 i_batch: 0.0 the loss for this batch: 0.21463917 flow loss 0.060492564 occ loss 0.15414426 time for this batch 0.23732829093933105 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.21952601 flow loss 0.061162215 occ loss 0.15836093 time for this batch 0.2623271942138672 ---------------------------------- train loss for this epoch: 0.228937
time for this epoch 41.65245056152344 No_decrease: 4 ----------------an epoch starts------------------- i_epoch: 78 # batch: 125 i_batch: 0.0 the loss for this batch: 0.2735226 flow loss 0.07392144 occ loss 0.1995973 time for this batch 0.21045351028442383 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.20558394 flow loss 0.06041336 occ loss 0.1451681 time for this batch 0.24865102767944336 ---------------------------------- train loss for this epoch: 0.226723
time for this epoch 41.4185836315155 No_decrease: 5 ----------------an epoch starts------------------- i_epoch: 79 # batch: 125 i_batch: 0.0 the loss for this batch: 0.2480248 flow loss 0.06411001 occ loss 0.18391171 time for this batch 0.22251486778259277 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.2630189 flow loss 0.07221432 occ loss 0.19080117 time for this batch 0.2653355598449707 ---------------------------------- train loss for this epoch: 0.227737
time for this epoch 41.366921186447144 No_decrease: 6 ----------------an epoch starts------------------- i_epoch: 80 # batch: 125 i_batch: 0.0 the loss for this batch: 0.25266457 flow loss 0.0667992 occ loss 0.18586211 time for this batch 0.20131540298461914 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.19769885 flow loss 0.054230843 occ loss 0.14346567 time for this batch 0.24327635765075684 ---------------------------------- train loss for this epoch: 0.227425
time for this epoch 41.3043327331543 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 81 # batch: 125 i_batch: 0.0 the loss for this batch: 0.22589727 flow loss 0.061502825 occ loss 0.16439104 time for this batch 0.2111802101135254 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.23598254 flow loss 0.06405857 occ loss 0.1719209 time for this batch 0.277996301651001 ---------------------------------- train loss for this epoch: 0.227004
time for this epoch 41.578349113464355 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 82 # batch: 125 i_batch: 0.0 the loss for this batch: 0.23954463 flow loss 0.07040868 occ loss 0.16913278 time for this batch 0.23908448219299316 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.19371219 flow loss 0.054814488 occ loss 0.1388952 time for this batch 0.28201961517333984 ---------------------------------- train loss for this epoch: 0.226786
time for this epoch 41.85551929473877 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 83 # batch: 125 i_batch: 0.0 the loss for this batch: 0.28767535 flow loss 0.07675408 occ loss 0.21091792 time for this batch 0.24402308464050293 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.27976662 flow loss 0.073091716 occ loss 0.20667177 time for this batch 0.22588157653808594 ---------------------------------- train loss for this epoch: 0.227563
time for this epoch 41.725703954696655 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 84 # batch: 125 i_batch: 0.0 the loss for this batch: 0.21414179 flow loss 0.056883052 occ loss 0.15725629 time for this batch 0.18756675720214844 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.24165039 flow loss 0.067020625 occ loss 0.17462677 time for this batch 0.2348341941833496 ---------------------------------- train loss for this epoch: 0.224935
time for this epoch 42.14080047607422 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 85 # batch: 125 i_batch: 0.0 the loss for this batch: 0.2617641 flow loss 0.078267835 occ loss 0.18349291 time for this batch 0.2258760929107666 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16613574 flow loss 0.052057356 occ loss 0.114076 time for this batch 0.28408026695251465 ---------------------------------- train loss for this epoch: 0.226011
time for this epoch 41.776161432266235 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 86 # batch: 125 i_batch: 0.0 the loss for this batch: 0.18891686 flow loss 0.056747716 occ loss 0.13216692 time for this batch 0.2205972671508789 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.21548948 flow loss 0.06683658 occ loss 0.14865018 time for this batch 0.2801394462585449 ---------------------------------- train loss for this epoch: 0.225803
time for this epoch 42.43686628341675 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 87 # batch: 125 i_batch: 0.0 the loss for this batch: 0.1831291 flow loss 0.053809185 occ loss 0.1293176 time for this batch 0.21290087699890137 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.26473922 flow loss 0.06795693 occ loss 0.19677907 time for this batch 0.27499842643737793 ---------------------------------- train loss for this epoch: 0.225634
time for this epoch 41.25823140144348 No_decrease: 4 ----------------an epoch starts------------------- i_epoch: 88 # batch: 125 i_batch: 0.0 the loss for this batch: 0.19337264 flow loss 0.057306495 occ loss 0.13606367 time for this batch 0.23572397232055664 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.22776407 flow loss 0.06347205 occ loss 0.16428901 time for this batch 0.2765076160430908 ---------------------------------- train loss for this epoch: 0.225452
time for this epoch 42.56925630569458 No_decrease: 5 ----------------an epoch starts------------------- i_epoch: 89 # batch: 125 i_batch: 0.0 the loss for this batch: 0.16001129 flow loss 0.048245482 occ loss 0.111763746 time for this batch 0.29604005813598633 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.24749418 flow loss 0.06199893 occ loss 0.18549249 time for this batch 0.30365562438964844 ---------------------------------- train loss for this epoch: 0.224004
time for this epoch 46.2707200050354 No_decrease: 6 ----------------an epoch starts------------------- i_epoch: 90 # batch: 125 i_batch: 0.0 the loss for this batch: 0.22294784 flow loss 0.05942814 occ loss 0.16351716 time for this batch 0.26371240615844727 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.20011124 flow loss 0.055606283 occ loss 0.1445023 time for this batch 0.295459508895874 ---------------------------------- train loss for this epoch: 0.225052
time for this epoch 45.46236515045166 No_decrease: 7 ----------------an epoch starts------------------- i_epoch: 91 # batch: 125 i_batch: 0.0 the loss for this batch: 0.34291768 flow loss 0.08866141 occ loss 0.25425255 time for this batch 0.2832016944885254 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.22634764 flow loss 0.062191118 occ loss 0.16415374 time for this batch 0.23416376113891602 ---------------------------------- train loss for this epoch: 0.22406
time for this epoch 41.62405347824097 No_decrease: 8 ----------------an epoch starts------------------- i_epoch: 92 # batch: 125 i_batch: 0.0 the loss for this batch: 0.1952047 flow loss 0.052248262 occ loss 0.14295407 time for this batch 0.20032691955566406 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.21942464 flow loss 0.05836923 occ loss 0.16105296 time for this batch 0.28084540367126465 ---------------------------------- train loss for this epoch: 0.2235
time for this epoch 41.527411222457886 No_decrease: 9 ----------------an epoch starts------------------- i_epoch: 93 # batch: 125 i_batch: 0.0 the loss for this batch: 0.26886383 flow loss 0.078737065 occ loss 0.19012289 time for this batch 0.22262954711914062 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.22621939 flow loss 0.066217184 occ loss 0.15999909 time for this batch 0.27254486083984375 ---------------------------------- train loss for this epoch: 0.223516
time for this epoch 41.7328884601593 No_decrease: 10 ----------------an epoch starts------------------- i_epoch: 94 # batch: 125 i_batch: 0.0 the loss for this batch: 0.236787 flow loss 0.061913874 occ loss 0.17487004 time for this batch 0.22897028923034668 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.21778944 flow loss 0.05553454 occ loss 0.16225208 time for this batch 0.27350282669067383 ---------------------------------- train loss for this epoch: 0.223679
time for this epoch 41.7143235206604 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 95 # batch: 125 i_batch: 0.0 the loss for this batch: 0.27300608 flow loss 0.07461398 occ loss 0.19838853 time for this batch 0.20975399017333984 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.20200396 flow loss 0.06363168 occ loss 0.13836966 time for this batch 0.27565836906433105 ---------------------------------- train loss for this epoch: 0.224834
time for this epoch 42.17789649963379 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 96 # batch: 125 i_batch: 0.0 the loss for this batch: 0.17571594 flow loss 0.052566424 occ loss 0.12314717 time for this batch 0.23063969612121582 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.27532274 flow loss 0.07213444 occ loss 0.20318468 time for this batch 0.24730634689331055 ---------------------------------- train loss for this epoch: 0.222347
time for this epoch 42.125614166259766 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 97 # batch: 125 i_batch: 0.0 the loss for this batch: 0.23883267 flow loss 0.06643714 occ loss 0.17239253 time for this batch 0.23118233680725098 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.22397767 flow loss 0.060420673 occ loss 0.16355409 time for this batch 0.25645899772644043 ---------------------------------- train loss for this epoch: 0.22237
time for this epoch 42.26485276222229 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 98 # batch: 125 i_batch: 0.0 the loss for this batch: 0.15756157 flow loss 0.04441195 occ loss 0.11314759 time for this batch 0.23082470893859863 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.23843364 flow loss 0.07451372 occ loss 0.16391629 time for this batch 0.2690558433532715 ---------------------------------- train loss for this epoch: 0.223393
time for this epoch 41.66188287734985 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 99 # batch: 125 i_batch: 0.0 the loss for this batch: 0.21583909 flow loss 0.061775044 occ loss 0.154061 time for this batch 0.20503544807434082 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.22177769 flow loss 0.06264668 occ loss 0.1591281 time for this batch 0.25884199142456055 ---------------------------------- train loss for this epoch: 0.222248
time for this epoch 41.87956666946411 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 100 # batch: 125 i_batch: 0.0 the loss for this batch: 0.25053006 flow loss 0.060978014 occ loss 0.18954886 time for this batch 0.2267923355102539 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.19535416 flow loss 0.059441708 occ loss 0.13590981 time for this batch 0.2742905616760254 ---------------------------------- train loss for this epoch: 0.222284
time for this epoch 41.88302159309387 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 101 # batch: 125 i_batch: 0.0 the loss for this batch: 0.23970485 flow loss 0.06254308 occ loss 0.17715895 time for this batch 0.2393786907196045 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15218155 flow loss 0.045845766 occ loss 0.10633378 time for this batch 0.2697463035583496 ---------------------------------- train loss for this epoch: 0.222588
time for this epoch 41.7710177898407 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 102 # batch: 125 i_batch: 0.0 the loss for this batch: 0.15445173 flow loss 0.04687271 occ loss 0.10757696 time for this batch 0.23672866821289062 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18465017 flow loss 0.05286251 occ loss 0.1317853 time for this batch 0.2666313648223877 ---------------------------------- train loss for this epoch: 0.221131
time for this epoch 42.182504653930664 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 103 # batch: 125 i_batch: 0.0 the loss for this batch: 0.2311126 flow loss 0.060325608 occ loss 0.1707843 time for this batch 0.21723723411560059 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.25649813 flow loss 0.06859391 occ loss 0.18790081 time for this batch 0.27520203590393066 ---------------------------------- train loss for this epoch: 0.222658
time for this epoch 41.94103455543518 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 104 # batch: 125 i_batch: 0.0 the loss for this batch: 0.22370605 flow loss 0.062660456 occ loss 0.16104253 time for this batch 0.29522228240966797 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18173878 flow loss 0.05446863 occ loss 0.12726784 time for this batch 0.28040480613708496 ---------------------------------- train loss for this epoch: 0.220782
time for this epoch 42.164156675338745 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 105 # batch: 125 i_batch: 0.0 the loss for this batch: 0.18182305 flow loss 0.051067382 occ loss 0.13075303 time for this batch 0.20215988159179688 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.25734118 flow loss 0.071897164 occ loss 0.18544061 time for this batch 0.26856136322021484 ---------------------------------- train loss for this epoch: 0.221138
time for this epoch 41.61906695365906 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 106 # batch: 125 i_batch: 0.0 the loss for this batch: 0.23199661 flow loss 0.0660961 occ loss 0.1658971 time for this batch 0.23978662490844727 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.29297143 flow loss 0.072449654 occ loss 0.22051835 time for this batch 0.2513697147369385 ---------------------------------- train loss for this epoch: 0.221434
time for this epoch 42.991281032562256 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 107 # batch: 125 i_batch: 0.0 the loss for this batch: 0.277559 flow loss 0.07470353 occ loss 0.20285168 time for this batch 0.21252703666687012 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.21174045 flow loss 0.060059644 occ loss 0.15167812 time for this batch 0.23051762580871582 ---------------------------------- train loss for this epoch: 0.219521
time for this epoch 37.33855581283569 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 108 # batch: 125 i_batch: 0.0 the loss for this batch: 0.18799287 flow loss 0.056978904 occ loss 0.13101165 time for this batch 0.19335341453552246 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.19736488 flow loss 0.056951374 occ loss 0.1404111 time for this batch 0.17429780960083008 ---------------------------------- train loss for this epoch: 0.220198
time for this epoch 32.094547271728516 No_decrease: 4 ----------------an epoch starts------------------- i_epoch: 109 # batch: 125 i_batch: 0.0 the loss for this batch: 0.19845124 flow loss 0.058537643 occ loss 0.13991082 time for this batch 0.14661407470703125 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.12900876 flow loss 0.04000761 occ loss 0.088999905 time for this batch 0.28094935417175293 ---------------------------------- train loss for this epoch: 0.221554
time for this epoch 40.52922701835632 No_decrease: 5 ----------------an epoch starts------------------- i_epoch: 110 # batch: 125 i_batch: 0.0 the loss for this batch: 0.29123098 flow loss 0.070914276 occ loss 0.2203127 time for this batch 0.24455022811889648 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.24196027 flow loss 0.06488582 occ loss 0.1770712 time for this batch 0.2831461429595947 ---------------------------------- train loss for this epoch: 0.221065
time for this epoch 43.47985100746155 No_decrease: 6 ----------------an epoch starts------------------- i_epoch: 111 # batch: 125 i_batch: 0.0 the loss for this batch: 0.21193945 flow loss 0.05807212 occ loss 0.15386422 time for this batch 0.21563148498535156 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17697644 flow loss 0.048886783 occ loss 0.12808712 time for this batch 0.2891218662261963 ---------------------------------- train loss for this epoch: 0.219304
time for this epoch 42.6476035118103 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 112 # batch: 125 i_batch: 0.0 the loss for this batch: 0.29074085 flow loss 0.08216046 occ loss 0.2085763 time for this batch 0.25671958923339844 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.23613 flow loss 0.065006256 occ loss 0.17112048 time for this batch 0.3067033290863037 ---------------------------------- train loss for this epoch: 0.219589
time for this epoch 46.06313991546631 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 113 # batch: 125 i_batch: 0.0 the loss for this batch: 0.278207 flow loss 0.07563792 occ loss 0.20256537 time for this batch 0.24155688285827637 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.22548349 flow loss 0.059422344 occ loss 0.16605827 time for this batch 0.2836167812347412 ---------------------------------- train loss for this epoch: 0.219324
time for this epoch 43.34487795829773 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 114 # batch: 125 i_batch: 0.0 the loss for this batch: 0.19846137 flow loss 0.05620601 occ loss 0.14225239 time for this batch 0.26648426055908203 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15912344 flow loss 0.04741393 occ loss 0.11170762 time for this batch 0.263167142868042 ---------------------------------- train loss for this epoch: 0.218526
time for this epoch 42.2772421836853 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 115 # batch: 125 i_batch: 0.0 the loss for this batch: 0.18201587 flow loss 0.0579648 occ loss 0.124048546 time for this batch 0.1976776123046875 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.25077718 flow loss 0.0643463 occ loss 0.1864277 time for this batch 0.2801206111907959 ---------------------------------- train loss for this epoch: 0.219103
time for this epoch 42.24750351905823 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 116 # batch: 125 i_batch: 0.0 the loss for this batch: 0.18177873 flow loss 0.054581765 occ loss 0.12719434 time for this batch 0.22018790245056152 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.29937106 flow loss 0.07773812 occ loss 0.2216291 time for this batch 0.23135662078857422 ---------------------------------- train loss for this epoch: 0.219595
time for this epoch 41.77748346328735 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 117 # batch: 125 i_batch: 0.0 the loss for this batch: 0.13742715 flow loss 0.04418909 occ loss 0.093236126 time for this batch 0.20531010627746582 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.23492035 flow loss 0.06773365 occ loss 0.1671833 time for this batch 0.27465033531188965 ---------------------------------- train loss for this epoch: 0.21866
time for this epoch 41.93105149269104 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 118 # batch: 125 i_batch: 0.0 the loss for this batch: 0.19706239 flow loss 0.052432064 occ loss 0.14462781 time for this batch 0.34674501419067383 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17894603 flow loss 0.0544722 occ loss 0.1244713 time for this batch 0.282517671585083 ---------------------------------- train loss for this epoch: 0.218751
time for this epoch 42.9322452545166 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 119 # batch: 125 i_batch: 0.0 the loss for this batch: 0.19345264 flow loss 0.049019575 occ loss 0.14443098 time for this batch 0.2166001796722412 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18726231 flow loss 0.05712756 occ loss 0.1301323 time for this batch 0.2755732536315918 ---------------------------------- train loss for this epoch: 0.219595
time for this epoch 42.20428681373596 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 120 # batch: 125 i_batch: 0.0 the loss for this batch: 0.22263347 flow loss 0.058179308 occ loss 0.16445121 time for this batch 0.22710275650024414 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.21619865 flow loss 0.06259569 occ loss 0.15360019 time for this batch 0.2814900875091553 ---------------------------------- train loss for this epoch: 0.2183
time for this epoch 42.39837670326233 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 121 # batch: 125 i_batch: 0.0 the loss for this batch: 0.21838075 flow loss 0.059964526 occ loss 0.15841326 time for this batch 0.21544599533081055 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18982664 flow loss 0.05843541 occ loss 0.13138835 time for this batch 0.2761216163635254 ---------------------------------- train loss for this epoch: 0.218151
time for this epoch 41.94264364242554 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 122 # batch: 125 i_batch: 0.0 the loss for this batch: 0.25425878 flow loss 0.06629129 occ loss 0.18796396 time for this batch 0.21388483047485352 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.2440179 flow loss 0.064330176 occ loss 0.17968452 time for this batch 0.25794148445129395 ---------------------------------- train loss for this epoch: 0.218036
time for this epoch 40.394192695617676 No_decrease: 4 ----------------an epoch starts------------------- i_epoch: 123 # batch: 125 i_batch: 0.0 the loss for this batch: 0.24709047 flow loss 0.06817693 occ loss 0.17890991 time for this batch 0.2148280143737793 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.19023246 flow loss 0.056499675 occ loss 0.13373013 time for this batch 0.29401683807373047 ---------------------------------- train loss for this epoch: 0.217708
time for this epoch 42.12530708312988 No_decrease: 5 ----------------an epoch starts------------------- i_epoch: 124 # batch: 125 i_batch: 0.0 the loss for this batch: 0.19413736 flow loss 0.056859195 occ loss 0.13727532 time for this batch 0.2203385829925537 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18943799 flow loss 0.053025983 occ loss 0.13640949 time for this batch 0.2505354881286621 ---------------------------------- train loss for this epoch: 0.218059
time for this epoch 42.186087131500244 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 125 # batch: 125 i_batch: 0.0 the loss for this batch: 0.1786645 flow loss 0.049970746 occ loss 0.12869129 time for this batch 0.22788262367248535 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.27100927 flow loss 0.06846429 occ loss 0.20254134 time for this batch 0.2770829200744629 ---------------------------------- train loss for this epoch: 0.218475
time for this epoch 44.13061261177063 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 126 # batch: 125 i_batch: 0.0 the loss for this batch: 0.24293362 flow loss 0.0688357 occ loss 0.17409459 time for this batch 0.26584768295288086 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.20193446 flow loss 0.05195322 occ loss 0.14997841 time for this batch 0.2739095687866211 ---------------------------------- train loss for this epoch: 0.217585
time for this epoch 41.20704174041748 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 127 # batch: 125 i_batch: 0.0 the loss for this batch: 0.25962692 flow loss 0.07085204 occ loss 0.18877153 time for this batch 0.2411355972290039 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.30560085 flow loss 0.079265736 occ loss 0.2263312 time for this batch 0.2752559185028076 ---------------------------------- train loss for this epoch: 0.216929
time for this epoch 74.57909035682678 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 128 # batch: 125 i_batch: 0.0 the loss for this batch: 0.21172437 flow loss 0.05990615 occ loss 0.15181547 time for this batch 0.3999898433685303 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.22937457 flow loss 0.063131236 occ loss 0.16624044 time for this batch 0.22456932067871094 ---------------------------------- train loss for this epoch: 0.217651
time for this epoch 41.94416117668152 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 129 # batch: 125 i_batch: 0.0 the loss for this batch: 0.28811038 flow loss 0.07567864 occ loss 0.21242768 time for this batch 0.23704290390014648 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.21532606 flow loss 0.059957284 occ loss 0.15536605 time for this batch 0.22739338874816895 ---------------------------------- train loss for this epoch: 0.217561
time for this epoch 42.19829058647156 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 130 # batch: 125 i_batch: 0.0 the loss for this batch: 0.23525964 flow loss 0.06170663 occ loss 0.17355 time for this batch 0.20319724082946777 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.24856043 flow loss 0.06999074 occ loss 0.17856641 time for this batch 0.2804100513458252 ---------------------------------- train loss for this epoch: 0.218016
time for this epoch 42.40673041343689 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 131 # batch: 125 i_batch: 0.0 the loss for this batch: 0.23918913 flow loss 0.06376412 occ loss 0.17542183 time for this batch 0.18825602531433105 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.22942914 flow loss 0.0629586 occ loss 0.16646716 time for this batch 0.27534055709838867 ---------------------------------- train loss for this epoch: 0.216223
time for this epoch 41.292866706848145 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 132 # batch: 125 i_batch: 0.0 the loss for this batch: 0.24199821 flow loss 0.06203023 occ loss 0.17996466 time for this batch 0.22184085845947266 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.2820892 flow loss 0.072885446 occ loss 0.2092001 time for this batch 0.19169306755065918 ---------------------------------- train loss for this epoch: 0.216215
time for this epoch 40.32429528236389 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 133 # batch: 125 i_batch: 0.0 the loss for this batch: 0.18958165 flow loss 0.054458637 occ loss 0.1351204 time for this batch 0.23932790756225586 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.21371515 flow loss 0.05739487 occ loss 0.15631782 time for this batch 0.25901174545288086 ---------------------------------- train loss for this epoch: 0.217879
time for this epoch 42.181485414505005 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 134 # batch: 125 i_batch: 0.0 the loss for this batch: 0.27856785 flow loss 0.06971441 occ loss 0.20885055 time for this batch 0.20453763008117676 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.21771833 flow loss 0.055815127 occ loss 0.16190045 time for this batch 0.22273612022399902 ---------------------------------- train loss for this epoch: 0.215532
time for this epoch 41.18204712867737 No_decrease: 4 ----------------an epoch starts------------------- i_epoch: 135 # batch: 125 i_batch: 0.0 the loss for this batch: 0.17137302 flow loss 0.046602666 occ loss 0.12476797 time for this batch 0.22974276542663574 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18884005 flow loss 0.054760553 occ loss 0.13407692 time for this batch 0.23755669593811035 ---------------------------------- train loss for this epoch: 0.217118
time for this epoch 42.76363134384155 No_decrease: 5 ----------------an epoch starts------------------- i_epoch: 136 # batch: 125 i_batch: 0.0 the loss for this batch: 0.23570749 flow loss 0.06291287 occ loss 0.17279144 time for this batch 0.24987459182739258 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1890979 flow loss 0.057762522 occ loss 0.13133268 time for this batch 0.2755556106567383 ---------------------------------- train loss for this epoch: 0.216306
time for this epoch 41.99428653717041 No_decrease: 6 ----------------an epoch starts------------------- i_epoch: 137 # batch: 125 i_batch: 0.0 the loss for this batch: 0.1800248 flow loss 0.052529756 occ loss 0.12749246 time for this batch 0.22927284240722656 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.2831176 flow loss 0.06786663 occ loss 0.21524744 time for this batch 0.2647056579589844 ---------------------------------- train loss for this epoch: 0.215089
time for this epoch 40.77215242385864 No_decrease: 7 ----------------an epoch starts------------------- i_epoch: 138 # batch: 125 i_batch: 0.0 the loss for this batch: 0.21692424 flow loss 0.060094073 occ loss 0.15682708 time for this batch 0.2267289161682129 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.19249007 flow loss 0.054648098 occ loss 0.13783923 time for this batch 0.27603936195373535 ---------------------------------- train loss for this epoch: 0.215356
time for this epoch 41.9412362575531 No_decrease: 8 ----------------an epoch starts------------------- i_epoch: 139 # batch: 125 i_batch: 0.0 the loss for this batch: 0.25906718 flow loss 0.07149022 occ loss 0.1875733 time for this batch 0.24434947967529297 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.21534374 flow loss 0.06377142 occ loss 0.1515694 time for this batch 0.26855993270874023 ---------------------------------- train loss for this epoch: 0.216722
time for this epoch 41.282649993896484 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 140 # batch: 125 i_batch: 0.0 the loss for this batch: 0.1356153 flow loss 0.040341865 occ loss 0.09527175 time for this batch 0.21072745323181152 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.22091088 flow loss 0.058896802 occ loss 0.16201137 time for this batch 0.2796821594238281 ---------------------------------- train loss for this epoch: 0.215606
time for this epoch 41.77755355834961 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 141 # batch: 125 i_batch: 0.0 the loss for this batch: 0.15888327 flow loss 0.047491554 occ loss 0.111389406 time for this batch 0.22901582717895508 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.2138165 flow loss 0.05819419 occ loss 0.15561962 time for this batch 0.2518174648284912 ---------------------------------- train loss for this epoch: 0.215878
time for this epoch 41.60721302032471 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 142 # batch: 125 i_batch: 0.0 the loss for this batch: 0.26242977 flow loss 0.06665526 occ loss 0.19577126 time for this batch 0.20580816268920898 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18734731 flow loss 0.05320092 occ loss 0.13414395 time for this batch 0.2843153476715088 ---------------------------------- train loss for this epoch: 0.215491
time for this epoch 41.74123525619507 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 143 # batch: 125 i_batch: 0.0 the loss for this batch: 0.15428059 flow loss 0.046939764 occ loss 0.107338905 time for this batch 0.2222905158996582 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.27171588 flow loss 0.070128106 occ loss 0.20158462 time for this batch 0.283750057220459 ---------------------------------- train loss for this epoch: 0.215411
time for this epoch 41.622607707977295 No_decrease: 4 ----------------an epoch starts------------------- i_epoch: 144 # batch: 125 i_batch: 0.0 the loss for this batch: 0.17428474 flow loss 0.04469056 occ loss 0.12959199 time for this batch 0.23050904273986816 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.21384189 flow loss 0.061130956 occ loss 0.15270762 time for this batch 0.2815978527069092 ---------------------------------- train loss for this epoch: 0.215557
time for this epoch 41.570964336395264 No_decrease: 5 ----------------an epoch starts------------------- i_epoch: 145 # batch: 125 i_batch: 0.0 the loss for this batch: 0.2227435 flow loss 0.062598005 occ loss 0.16014248 time for this batch 0.23709583282470703 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.20244241 flow loss 0.056272995 occ loss 0.14616685 time for this batch 0.2829272747039795 ---------------------------------- train loss for this epoch: 0.214672
time for this epoch 41.465089082717896 No_decrease: 6 ----------------an epoch starts------------------- i_epoch: 146 # batch: 125 i_batch: 0.0 the loss for this batch: 0.2647494 flow loss 0.06440261 occ loss 0.20034313 time for this batch 0.211226224899292 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.23965442 flow loss 0.065159954 occ loss 0.1744914 time for this batch 0.29022955894470215 ---------------------------------- train loss for this epoch: 0.214063
time for this epoch 43.30857443809509 No_decrease: 7 ----------------an epoch starts------------------- i_epoch: 147 # batch: 125 i_batch: 0.0 the loss for this batch: 0.30577043 flow loss 0.07431197 occ loss 0.23145455 time for this batch 0.18227815628051758 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.21118817 flow loss 0.063556805 occ loss 0.14762825 time for this batch 0.265033483505249 ---------------------------------- train loss for this epoch: 0.214866
time for this epoch 39.934157371520996 No_decrease: 8 ----------------an epoch starts------------------- i_epoch: 148 # batch: 125 i_batch: 0.0 the loss for this batch: 0.19139647 flow loss 0.053137064 occ loss 0.13825677 time for this batch 0.3109135627746582 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.2068136 flow loss 0.054955795 occ loss 0.15185468 time for this batch 0.23604798316955566 ---------------------------------- train loss for this epoch: 0.214022
time for this epoch 41.79554605484009 No_decrease: 9 ----------------an epoch starts------------------- i_epoch: 149 # batch: 125 i_batch: 0.0 the loss for this batch: 0.21289837 flow loss 0.056360964 occ loss 0.15653461 time for this batch 0.24272370338439941 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.19420256 flow loss 0.055928875 occ loss 0.13827124 time for this batch 0.22690916061401367 ---------------------------------- train loss for this epoch: 0.214405
time for this epoch 42.459909439086914 No_decrease: 10 ----------------an epoch starts------------------- i_epoch: 150 # batch: 125 i_batch: 0.0 the loss for this batch: 0.26758417 flow loss 0.06630538 occ loss 0.20127577 time for this batch 0.21882295608520508 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1860262 flow loss 0.052577846 occ loss 0.13344596 time for this batch 0.26081275939941406 ---------------------------------- train loss for this epoch: 0.207508
time for this epoch 42.84970164299011 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 151 # batch: 125 i_batch: 0.0 the loss for this batch: 0.21838078 flow loss 0.055563807 occ loss 0.16281414 time for this batch 0.23534607887268066 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.22131611 flow loss 0.05806899 occ loss 0.16324425 time for this batch 0.2843050956726074 ---------------------------------- train loss for this epoch: 0.206561
time for this epoch 42.37132787704468 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 152 # batch: 125 i_batch: 0.0 the loss for this batch: 0.2561783 flow loss 0.06876506 occ loss 0.18740949 time for this batch 0.23401117324829102 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.21862979 flow loss 0.05661908 occ loss 0.162008 time for this batch 0.19288325309753418 ---------------------------------- train loss for this epoch: 0.205413
time for this epoch 41.8275203704834 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 153 # batch: 125 i_batch: 0.0 the loss for this batch: 0.2063981 flow loss 0.05999014 occ loss 0.14640503 time for this batch 0.2424464225769043 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.20794451 flow loss 0.055647932 occ loss 0.15229343 time for this batch 0.2602558135986328 ---------------------------------- train loss for this epoch: 0.205591
time for this epoch 41.996498584747314 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 154 # batch: 125 i_batch: 0.0 the loss for this batch: 0.23400104 flow loss 0.06513864 occ loss 0.16885887 time for this batch 0.23223328590393066 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.21544784 flow loss 0.05736001 occ loss 0.15808438 time for this batch 0.28777337074279785 ---------------------------------- train loss for this epoch: 0.205944
time for this epoch 42.011351585388184 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 155 # batch: 125 i_batch: 0.0 the loss for this batch: 0.26626837 flow loss 0.06582797 occ loss 0.20043662 time for this batch 0.24588298797607422 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.24651918 flow loss 0.06264786 occ loss 0.18386777 time for this batch 0.26252031326293945 ---------------------------------- train loss for this epoch: 0.20563
time for this epoch 41.38074707984924 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 156 # batch: 125 i_batch: 0.0 the loss for this batch: 0.25792325 flow loss 0.06390717 occ loss 0.19401288 time for this batch 0.21559596061706543 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.2176674 flow loss 0.06022596 occ loss 0.15743865 time for this batch 0.25992822647094727 ---------------------------------- train loss for this epoch: 0.205594
time for this epoch 41.72551679611206 No_decrease: 4 ----------------an epoch starts------------------- i_epoch: 157 # batch: 125 i_batch: 0.0 the loss for this batch: 0.2294323 flow loss 0.062635526 occ loss 0.16679345 time for this batch 0.24199151992797852 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15671913 flow loss 0.044516373 occ loss 0.112200655 time for this batch 0.27823305130004883 ---------------------------------- train loss for this epoch: 0.205645
time for this epoch 41.39026594161987 No_decrease: 5 ----------------an epoch starts------------------- i_epoch: 158 # batch: 125 i_batch: 0.0 the loss for this batch: 0.1835721 flow loss 0.050661433 occ loss 0.13290808 time for this batch 0.28292417526245117 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.26503032 flow loss 0.071869746 occ loss 0.19315697 time for this batch 0.30738186836242676 ---------------------------------- train loss for this epoch: 0.205669
time for this epoch 43.35257077217102 No_decrease: 6 ----------------an epoch starts------------------- i_epoch: 159 # batch: 125 i_batch: 0.0 the loss for this batch: 0.12965152 flow loss 0.041757625 occ loss 0.08789203 time for this batch 0.24086260795593262 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.19814679 flow loss 0.056158993 occ loss 0.14198492 time for this batch 0.2850682735443115 ---------------------------------- train loss for this epoch: 0.205507
time for this epoch 43.118030309677124 No_decrease: 7 ----------------an epoch starts------------------- i_epoch: 160 # batch: 125 i_batch: 0.0 the loss for this batch: 0.18234546 flow loss 0.050439455 occ loss 0.13190348 time for this batch 0.22597646713256836 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16208196 flow loss 0.044372242 occ loss 0.11770737 time for this batch 0.30304718017578125 ---------------------------------- train loss for this epoch: 0.205206
time for this epoch 43.394784688949585 No_decrease: 8 ----------------an epoch starts------------------- i_epoch: 161 # batch: 125 i_batch: 0.0 the loss for this batch: 0.17577036 flow loss 0.04854952 occ loss 0.12721846 time for this batch 0.24975943565368652 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.22403318 flow loss 0.06213473 occ loss 0.1618952 time for this batch 0.2562134265899658 ---------------------------------- train loss for this epoch: 0.205484
time for this epoch 43.60386610031128 No_decrease: 9 ----------------an epoch starts------------------- i_epoch: 162 # batch: 125 i_batch: 0.0 the loss for this batch: 0.21644096 flow loss 0.057327032 occ loss 0.15911084 time for this batch 0.25058412551879883 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14962867 flow loss 0.0462454 occ loss 0.10338128 time for this batch 0.25864458084106445 ---------------------------------- train loss for this epoch: 0.205237
time for this epoch 44.75456666946411 No_decrease: 10 ----------------an epoch starts------------------- i_epoch: 163 # batch: 125 i_batch: 0.0 the loss for this batch: 0.1695727 flow loss 0.04843332 occ loss 0.12113708 time for this batch 0.21361446380615234 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.20049307 flow loss 0.055868294 occ loss 0.14462176 time for this batch 0.40930628776550293 ---------------------------------- train loss for this epoch: 0.205138
time for this epoch 42.40977454185486 No_decrease: 11 ----------------an epoch starts------------------- i_epoch: 164 # batch: 125 i_batch: 0.0 the loss for this batch: 0.17399058 flow loss 0.055794474 occ loss 0.118193515 time for this batch 0.23539519309997559 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.26454535 flow loss 0.070985876 occ loss 0.19355574 time for this batch 0.24124908447265625 ---------------------------------- train loss for this epoch: 0.204947
time for this epoch 41.69672465324402 No_decrease: 12 ----------------an epoch starts------------------- i_epoch: 165 # batch: 125 i_batch: 0.0 the loss for this batch: 0.21871454 flow loss 0.05852745 occ loss 0.16018379 time for this batch 0.254474401473999 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16516091 flow loss 0.046492703 occ loss 0.11866586 time for this batch 0.28757166862487793 ---------------------------------- train loss for this epoch: 0.205299
time for this epoch 43.10002088546753 No_decrease: 13 ----------------an epoch starts------------------- i_epoch: 166 # batch: 125 i_batch: 0.0 the loss for this batch: 0.20719512 flow loss 0.057472095 occ loss 0.14971986 time for this batch 0.25737738609313965 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.22925708 flow loss 0.058483463 occ loss 0.17077073 time for this batch 0.25655293464660645 ---------------------------------- train loss for this epoch: 0.204842
time for this epoch 42.00644850730896 No_decrease: 14 ----------------an epoch starts------------------- i_epoch: 167 # batch: 125 i_batch: 0.0 the loss for this batch: 0.16968362 flow loss 0.04656805 occ loss 0.12311305 time for this batch 0.2137317657470703 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.230568 flow loss 0.058031835 occ loss 0.1725329 time for this batch 0.22789525985717773 ---------------------------------- train loss for this epoch: 0.204953
time for this epoch 41.83325934410095 No_decrease: 15 ----------------an epoch starts------------------- i_epoch: 168 # batch: 125 i_batch: 0.0 the loss for this batch: 0.17287338 flow loss 0.0489288 occ loss 0.12394205 time for this batch 0.2243490219116211 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.21835619 flow loss 0.057922743 occ loss 0.16043034 time for this batch 0.2635667324066162 ---------------------------------- train loss for this epoch: 0.204714
time for this epoch 42.79268288612366 No_decrease: 16 ----------------an epoch starts------------------- i_epoch: 169 # batch: 125 i_batch: 0.0 the loss for this batch: 0.1489692 flow loss 0.04259062 occ loss 0.10637648 time for this batch 0.2522766590118408 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.25594637 flow loss 0.06328515 occ loss 0.19265784 time for this batch 0.21370935440063477 ---------------------------------- train loss for this epoch: 0.204752
time for this epoch 34.54697632789612 No_decrease: 17 ----------------an epoch starts------------------- i_epoch: 170 # batch: 125 i_batch: 0.0 the loss for this batch: 0.19134444 flow loss 0.05356236 occ loss 0.13777943 time for this batch 0.2964956760406494 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.12377656 flow loss 0.038140032 occ loss 0.08563481 time for this batch 0.3330874443054199 ---------------------------------- train loss for this epoch: 0.204713
time for this epoch 44.99956250190735 No_decrease: 18 ----------------an epoch starts------------------- i_epoch: 171 # batch: 125 i_batch: 0.0 the loss for this batch: 0.17428608 flow loss 0.04655173 occ loss 0.12773235 time for this batch 0.23907923698425293 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.25466868 flow loss 0.06506466 occ loss 0.18960054 time for this batch 0.22305774688720703 ---------------------------------- train loss for this epoch: 0.204913
time for this epoch 39.15002751350403 No_decrease: 19 ----------------an epoch starts------------------- i_epoch: 172 # batch: 125 i_batch: 0.0 the loss for this batch: 0.2418112 flow loss 0.06960455 occ loss 0.17220354 time for this batch 0.24449515342712402 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.20872624 flow loss 0.059464402 occ loss 0.14925897 time for this batch 0.28780531883239746 ---------------------------------- train loss for this epoch: 0.204235
time for this epoch 43.31948447227478 No_decrease: 20 ----------------an epoch starts------------------- i_epoch: 173 # batch: 125 i_batch: 0.0 the loss for this batch: 0.17534937 flow loss 0.051361926 occ loss 0.12398487 time for this batch 0.23879241943359375 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1911637 flow loss 0.05308005 occ loss 0.13808072 time for this batch 0.2928144931793213 ---------------------------------- train loss for this epoch: 0.204676
time for this epoch 41.69868731498718 No_decrease: 21 ----------------an epoch starts------------------- i_epoch: 174 # batch: 125 i_batch: 0.0 the loss for this batch: 0.21232168 flow loss 0.05608559 occ loss 0.15623324 time for this batch 0.23205041885375977 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16554348 flow loss 0.049815714 occ loss 0.11572546 time for this batch 0.23040246963500977 ---------------------------------- train loss for this epoch: 0.204722
time for this epoch 42.11300277709961 No_decrease: 22 ----------------an epoch starts------------------- i_epoch: 175 # batch: 125 i_batch: 0.0 the loss for this batch: 0.18218865 flow loss 0.055352807 occ loss 0.12683322 time for this batch 0.19911789894104004 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.19288406 flow loss 0.05811435 occ loss 0.134767 time for this batch 0.260974645614624 ---------------------------------- train loss for this epoch: 0.204493
time for this epoch 41.75030708312988 No_decrease: 23 ----------------an epoch starts------------------- i_epoch: 176 # batch: 125 i_batch: 0.0 the loss for this batch: 0.19099781 flow loss 0.051372446 occ loss 0.13962275 time for this batch 0.26321887969970703 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16621423 flow loss 0.04818902 occ loss 0.11802298 time for this batch 0.26686596870422363 ---------------------------------- train loss for this epoch: 0.204523
time for this epoch 41.77340841293335 No_decrease: 24 ----------------an epoch starts------------------- i_epoch: 177 # batch: 125 i_batch: 0.0 the loss for this batch: 0.19908738 flow loss 0.056425314 occ loss 0.14265929 time for this batch 0.22936248779296875 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18749772 flow loss 0.054349273 occ loss 0.13314578 time for this batch 0.26246166229248047 ---------------------------------- train loss for this epoch: 0.204394
time for this epoch 42.009405851364136 No_decrease: 25 ----------------an epoch starts------------------- i_epoch: 178 # batch: 125 i_batch: 0.0 the loss for this batch: 0.21519817 flow loss 0.05989522 occ loss 0.15529965 time for this batch 0.21381640434265137 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.22106177 flow loss 0.060625277 occ loss 0.16043341 time for this batch 0.29407739639282227 ---------------------------------- train loss for this epoch: 0.204114
time for this epoch 40.89222431182861 No_decrease: 26 ----------------an epoch starts------------------- i_epoch: 179 # batch: 125 i_batch: 0.0 the loss for this batch: 0.21283524 flow loss 0.05645304 occ loss 0.15637906 time for this batch 0.25467824935913086 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.23521602 flow loss 0.06693491 occ loss 0.16827779 time for this batch 0.25475311279296875 ---------------------------------- train loss for this epoch: 0.204356
time for this epoch 42.15910220146179 No_decrease: 27 ----------------an epoch starts------------------- i_epoch: 180 # batch: 125 i_batch: 0.0 the loss for this batch: 0.22205174 flow loss 0.058937307 occ loss 0.16311118 time for this batch 0.26519274711608887 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.19125162 flow loss 0.05429957 occ loss 0.13694932 time for this batch 0.25635862350463867 ---------------------------------- train loss for this epoch: 0.204259
time for this epoch 42.96978282928467 No_decrease: 28 ----------------an epoch starts------------------- i_epoch: 181 # batch: 125 i_batch: 0.0 the loss for this batch: 0.1611499 flow loss 0.05323099 occ loss 0.1079165 time for this batch 0.20953798294067383 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.24725711 flow loss 0.06226711 occ loss 0.1849865 time for this batch 0.23098969459533691 ---------------------------------- train loss for this epoch: 0.204084
time for this epoch 41.2967963218689 No_decrease: 29 ----------------an epoch starts------------------- i_epoch: 182 # batch: 125 i_batch: 0.0 the loss for this batch: 0.21133523 flow loss 0.056435525 occ loss 0.15489668 time for this batch 0.24483084678649902 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.20561323 flow loss 0.053590134 occ loss 0.1520201 time for this batch 0.27539753913879395 ---------------------------------- train loss for this epoch: 0.204336
time for this epoch 41.0474693775177 Early stop at the 183-th epoch
def apply_to_vali_test(model, vt, f_o_mean_std):
f = vt["flow"]
f_m = vt["flow_mask"].to(device)
o = vt["occupancy"]
o_m = vt["occupancy_mask"].to(device)
f_mae, f_rmse, o_mae, o_rmse = vali_test(model, f, f_m, o, o_m, f_o_mean_std, hyper["b_s_vt"])
print ("flow_mae", f_mae)
print ("flow_rmse", f_rmse)
print ("occ_mae", o_mae)
print ("occ_rmse", o_rmse)
return f_mae, f_rmse, o_mae, o_rmse
vali_f_mae, vali_f_rmse, vali_o_mae, vali_o_rmse =\
apply_to_vali_test(trained_model, vali, f_o_mean_std)
flow_mae 35.706013219496114 flow_rmse 55.067228280538785 occ_mae 0.03881964723069924 occ_rmse 0.07973054776639152
test_f_mae, test_f_rmse, test_o_mae, test_o_rmse =\
apply_to_vali_test(trained_model, test, f_o_mean_std)
flow_mae 33.70526106641875 flow_rmse 52.30127946606076 occ_mae 0.03179610162832142 occ_rmse 0.06858186673505016